Pitfalls in machine learning‐based assessment of tumor‐infiltrating lymphocytes in breast cancer: A report of the International Immuno‐Oncology Biomarker Working …
The clinical significance of the tumor‐immune interaction in breast cancer is now
established, and tumor‐infiltrating lymphocytes (TILs) have emerged as predictive and …
established, and tumor‐infiltrating lymphocytes (TILs) have emerged as predictive and …
[HTML][HTML] Computational pathology: a survey review and the way forward
Abstract Computational Pathology (CPath) is an interdisciplinary science that augments
developments of computational approaches to analyze and model medical histopathology …
developments of computational approaches to analyze and model medical histopathology …
[HTML][HTML] One model is all you need: multi-task learning enables simultaneous histology image segmentation and classification
The recent surge in performance for image analysis of digitised pathology slides can largely
be attributed to the advances in deep learning. Deep models can be used to initially localise …
be attributed to the advances in deep learning. Deep models can be used to initially localise …
Bracs: A dataset for breast carcinoma subty** in h&e histology images
Breast cancer is the most commonly diagnosed cancer and registers the highest number of
deaths for women. Advances in diagnostic activities combined with large-scale screening …
deaths for women. Advances in diagnostic activities combined with large-scale screening …
AI-enabled routine H&E image based prognostic marker for early-stage luminal breast cancer
Breast cancer (BC) grade is a well-established subjective prognostic indicator of tumour
aggressiveness. Tumour heterogeneity and subjective assessment result in high degree of …
aggressiveness. Tumour heterogeneity and subjective assessment result in high degree of …
[HTML][HTML] CoNIC Challenge: Pushing the frontiers of nuclear detection, segmentation, classification and counting
Nuclear detection, segmentation and morphometric profiling are essential in hel** us
further understand the relationship between histology and patient outcome. To drive …
further understand the relationship between histology and patient outcome. To drive …
Evaluation of tumour infiltrating lymphocytes in luminal breast cancer using artificial intelligence
Abstract Background Tumour infiltrating lymphocytes (TILs) are a prognostic parameter in
triple-negative and human epidermal growth factor receptor 2 (HER2)-positive breast cancer …
triple-negative and human epidermal growth factor receptor 2 (HER2)-positive breast cancer …
Unleashing the potential of AI for pathology: challenges and recommendations
Computational pathology is currently witnessing a surge in the development of AI
techniques, offering promise for achieving breakthroughs and significantly impacting the …
techniques, offering promise for achieving breakthroughs and significantly impacting the …
[HTML][HTML] Annotating for artificial intelligence applications in digital pathology: a practical guide for pathologists and researchers
Training machine learning models for artificial intelligence (AI) applications in pathology
often requires extensive annotation by human experts, but there is little guidance on the …
often requires extensive annotation by human experts, but there is little guidance on the …
[HTML][HTML] Artificial intelligence-based mitosis scoring in breast cancer: Clinical application
In recent years, artificial intelligence (AI) has demonstrated exceptional performance in
mitosis identification and quantification. However, the implementation of AI in clinical …
mitosis identification and quantification. However, the implementation of AI in clinical …